Lab 14 Homework
Mildred Hernandez
2021-03-04
Instructions
Answer the following questions and complete the exercises in RMarkdown. Please embed all of your code and push your final work to your repository. Your final lab report should be organized, clean, and run free from errors. Remember, you must remove the # for the included code chunks to run. Be sure to add your name to the author header above. For any included plots, make sure they are clearly labeled. You are free to use any plot type that you feel best communicates the results of your analysis.
Make sure to use the formatting conventions of RMarkdown to make your report neat and clean!
Load the libraries
library(qtl)
library(qtlcharts)
library(tidyverse)
library(ggmap)1. We have a satellite collars on a number of different individuals and want to be able to quickly look at all of their recent movements at once. Please load all the data files from us_individual_collar_data and use for loop to create plots for all different individuals of the path they move on longitude and latitude.
us_individual_collar_files <- list.files("data/us_individual_collar_data", full.names = TRUE)
us_individual_collar_files## [1] "data/us_individual_collar_data/collar-data-A1-2016-02-26.txt"
## [2] "data/us_individual_collar_data/collar-data-B2-2016-02-26.txt"
## [3] "data/us_individual_collar_data/collar-data-C3-2016-02-26.txt"
## [4] "data/us_individual_collar_data/collar-data-D4-2016-02-26.txt"
## [5] "data/us_individual_collar_data/collar-data-E5-2016-02-26.txt"
## [6] "data/us_individual_collar_data/collar-data-F6-2016-02-26.txt"
## [7] "data/us_individual_collar_data/collar-data-G7-2016-02-26.txt"
## [8] "data/us_individual_collar_data/collar-data-H8-2016-02-26.txt"
## [9] "data/us_individual_collar_data/collar-data-I9-2016-02-26.txt"
## [10] "data/us_individual_collar_data/collar-data-J10-2016-02-26.txt"# only ggplot
for (i in 1:length(us_individual_collar_files)){
us_individual_plots <- as.data.frame(read_csv(us_individual_collar_files[i]))
print(
ggplot(us_individual_plots, aes(x=long,y=lat))+
geom_path()+
geom_point()+
labs(title = "Collar Data", x = "Longitude", y = "Latitude")
)
}## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
2. Please load all the data files from us_individual_collar_data and combine all data into one data frame. Create a summary to show what is the maximum and minimum of recorded data points on longitude and latitude.
data_list <- lapply(us_individual_collar_files, read_csv)## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )us_collar_data_all <- bind_rows(data_list)
us_collar_data_all %>%
summarise(max_lat=max(lat),
min_lat=min(lat),
max_long=max(long),
min_long=min(long))## # A tibble: 1 x 4
## max_lat min_lat max_long min_long
## <dbl> <dbl> <dbl> <dbl>
## 1 41.6 26.6 -106. -123.3. Use the range of the latitude and longitude from Q2 to build an appropriate bounding box for your map and load a map from stamen in a terrain style projection and display the map. Then, build a final map that overlays the recorded path from Q1.
lat <- c(26.6116, 41.58802)
long <- c(-122.6017, -106.3343)
bbox <- make_bbox(long, lat, f = 0.5)
map <- get_map(bbox, maptype = "terrain", source = "stamen")## Source : http://tile.stamen.com/terrain/5/4/10.png## Source : http://tile.stamen.com/terrain/5/5/10.png## Source : http://tile.stamen.com/terrain/5/6/10.png## Source : http://tile.stamen.com/terrain/5/7/10.png## Source : http://tile.stamen.com/terrain/5/4/11.png## Source : http://tile.stamen.com/terrain/5/5/11.png## Source : http://tile.stamen.com/terrain/5/6/11.png## Source : http://tile.stamen.com/terrain/5/7/11.png## Source : http://tile.stamen.com/terrain/5/4/12.png## Source : http://tile.stamen.com/terrain/5/5/12.png## Source : http://tile.stamen.com/terrain/5/6/12.png## Source : http://tile.stamen.com/terrain/5/7/12.png## Source : http://tile.stamen.com/terrain/5/4/13.png## Source : http://tile.stamen.com/terrain/5/5/13.png## Source : http://tile.stamen.com/terrain/5/6/13.png## Source : http://tile.stamen.com/terrain/5/7/13.png## Source : http://tile.stamen.com/terrain/5/4/14.png## Source : http://tile.stamen.com/terrain/5/5/14.png## Source : http://tile.stamen.com/terrain/5/6/14.png## Source : http://tile.stamen.com/terrain/5/7/14.pngggmap(map)
lat <- c(26.6116, 41.58802)
long <- c(-122.6017, -106.3343)
bbox <- make_bbox(long, lat, f = 0.5)
map <- get_map(bbox, maptype = "terrain", source = "stamen")
for (i in 1:length(us_individual_collar_files)){
us_individual_plots <- as.data.frame(read_csv(us_individual_collar_files[i]))
print(
ggmap(map)+
geom_path(data = us_individual_plots, aes(long,lat))+
geom_point(data = us_individual_plots, aes(long,lat))+
labs(title = "Collar Data", x = "Longitude", y = "Latitude")
)
}## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## date = col_date(format = ""),
## collar = col_character(),
## time = col_datetime(format = ""),
## lat = col_double(),
## long = col_double()
## )
Load the Data
We will use the data from an experiment on hypertension in the mouse Sugiyama et al., Genomics 71:70-77, 2001
#?hyper
data(hyper)4. Create a summary of the hypertension data. How many individuals and phenotypes are included in this data set? How many gene markers and chromosomes are included in this data set? Please create a table to show the number of markers on each chromosome.
summary(hyper)## Backcross
##
## No. individuals: 250
##
## No. phenotypes: 2
## Percent phenotyped: 100 100
##
## No. chromosomes: 20
## Autosomes: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
## X chr: X
##
## Total markers: 174
## No. markers: 22 8 6 20 14 11 7 6 5 5 14 5 5 5 11 6 12 4 4 4
## Percent genotyped: 47.7
## Genotypes (%):
## Autosomes: BB:50.1 BA:49.9
## X chromosome: BY:53.0 AY:47.0nmar(hyper)## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 X
## 22 8 6 20 14 11 7 6 5 5 14 5 5 5 11 6 12 4 4 45. Please make an interactive genetic map of markers for the hypertension data.
iplotMap(hyper)## Set screen size to height=700 x width=10006. Make a plot that shows the pattern of missing genotype data in the hypertension dataset. Please reorder the recorded individuals according to their blood pressure phenotypes. Is there a specific pattern of missing genotype? Please explain it. Yes, researchers may sometimes only genotype individuals with extreme phenotypes.
hyper$pheno## bp sex
## 1 109.6 male
## 2 109.8 male
## 3 110.1 male
## 4 110.6 male
## 5 115.0 male
## 6 109.8 male
## 7 114.4 male
## 8 113.4 male
## 9 113.8 male
## 10 113.1 male
## 11 120.8 male
## 12 110.9 male
## 13 112.2 male
## 14 110.4 male
## 15 111.9 male
## 16 113.3 male
## 17 114.3 male
## 18 113.8 male
## 19 118.3 male
## 20 110.8 male
## 21 109.5 male
## 22 111.6 male
## 23 113.2 male
## 24 115.7 male
## 25 109.5 male
## 26 112.6 male
## 27 119.7 male
## 28 122.6 male
## 29 118.0 male
## 30 116.3 male
## 31 110.2 male
## 32 119.3 male
## 33 110.7 male
## 34 109.9 male
## 35 128.0 male
## 36 116.1 male
## 37 119.6 male
## 38 118.7 male
## 39 109.6 male
## 40 112.2 male
## 41 109.5 male
## 42 118.1 male
## 43 110.2 male
## 44 119.3 male
## 45 116.2 male
## 46 113.4 male
## 47 89.3 male
## 48 88.2 male
## 49 93.3 male
## 50 84.8 male
## 51 92.1 male
## 52 93.2 male
## 53 91.0 male
## 54 88.8 male
## 55 89.0 male
## 56 92.6 male
## 57 92.0 male
## 58 84.1 male
## 59 92.7 male
## 60 88.3 male
## 61 85.6 male
## 62 86.3 male
## 63 91.9 male
## 64 87.4 male
## 65 92.9 male
## 66 91.0 male
## 67 90.2 male
## 68 90.3 male
## 69 88.5 male
## 70 91.0 male
## 71 88.4 male
## 72 88.8 male
## 73 91.7 male
## 74 93.1 male
## 75 89.3 male
## 76 88.8 male
## 77 88.9 male
## 78 92.8 male
## 79 92.3 male
## 80 89.6 male
## 81 93.3 male
## 82 91.1 male
## 83 93.2 male
## 84 92.7 male
## 85 92.4 male
## 86 91.6 male
## 87 82.7 male
## 88 93.5 male
## 89 87.8 male
## 90 92.3 male
## 91 90.3 male
## 92 93.5 male
## 93 107.1 male
## 94 96.1 male
## 95 96.8 male
## 96 95.1 male
## 97 94.6 male
## 98 105.9 male
## 99 96.8 male
## 100 106.0 male
## 101 107.1 male
## 102 96.0 male
## 103 104.3 male
## 104 100.0 male
## 105 109.4 male
## 106 108.5 male
## 107 98.2 male
## 108 97.7 male
## 109 102.7 male
## 110 106.2 male
## 111 100.2 male
## 112 93.8 male
## 113 102.7 male
## 114 102.4 male
## 115 106.1 male
## 116 95.4 male
## 117 106.5 male
## 118 96.9 male
## 119 94.2 male
## 120 94.5 male
## 121 97.8 male
## 122 101.8 male
## 123 104.5 male
## 124 104.8 male
## 125 96.0 male
## 126 97.5 male
## 127 101.2 male
## 128 95.6 male
## 129 101.7 male
## 130 102.6 male
## 131 97.4 male
## 132 95.0 male
## 133 97.0 male
## 134 96.6 male
## 135 102.2 male
## 136 105.5 male
## 137 100.1 male
## 138 96.3 male
## 139 96.5 male
## 140 107.0 male
## 141 97.2 male
## 142 98.2 male
## 143 105.5 male
## 144 100.3 male
## 145 94.4 male
## 146 104.9 male
## 147 96.4 male
## 148 103.1 male
## 149 104.1 male
## 150 109.2 male
## 151 94.1 male
## 152 98.7 male
## 153 103.4 male
## 154 99.5 male
## 155 101.2 male
## 156 99.4 male
## 157 105.7 male
## 158 98.9 male
## 159 98.6 male
## 160 99.0 male
## 161 105.9 male
## 162 102.3 male
## 163 102.8 male
## 164 101.7 male
## 165 105.8 male
## 166 99.0 male
## 167 95.7 male
## 168 94.7 male
## 169 107.5 male
## 170 105.0 male
## 171 94.1 male
## 172 108.7 male
## 173 98.1 male
## 174 104.9 male
## 175 95.1 male
## 176 94.7 male
## 177 108.6 male
## 178 97.6 male
## 179 96.7 male
## 180 96.0 male
## 181 107.7 male
## 182 96.9 male
## 183 96.0 male
## 184 99.5 male
## 185 100.6 male
## 186 100.5 male
## 187 103.6 male
## 188 107.6 male
## 189 99.5 male
## 190 101.1 male
## 191 96.1 male
## 192 103.7 male
## 193 95.5 male
## 194 94.3 male
## 195 107.5 male
## 196 102.7 male
## 197 96.6 male
## 198 105.9 male
## 199 101.6 male
## 200 105.6 male
## 201 102.0 male
## 202 105.9 male
## 203 102.3 male
## 204 105.2 male
## 205 103.3 male
## 206 105.6 male
## 207 96.4 male
## 208 98.2 male
## 209 103.4 male
## 210 93.8 male
## 211 104.1 male
## 212 95.3 male
## 213 96.9 male
## 214 107.6 male
## 215 107.3 male
## 216 103.3 male
## 217 99.6 male
## 218 107.5 male
## 219 95.1 male
## 220 98.1 male
## 221 106.6 male
## 222 100.6 male
## 223 98.7 male
## 224 97.5 male
## 225 100.7 male
## 226 100.0 male
## 227 106.7 male
## 228 108.2 male
## 229 106.7 male
## 230 105.4 male
## 231 103.0 male
## 232 96.6 male
## 233 108.7 male
## 234 108.7 male
## 235 107.2 male
## 236 101.2 male
## 237 98.4 male
## 238 105.8 male
## 239 109.1 male
## 240 95.1 male
## 241 104.3 male
## 242 101.6 male
## 243 95.6 male
## 244 109.2 male
## 245 109.3 male
## 246 98.8 male
## 247 116.2 male
## 248 100.8 male
## 249 106.7 male
## 250 98.5 maleplotMissing(hyper, main="")
plotMissing(hyper, main="", reorder=1)
7. Based on your answer from previous question, you probably noticed that there are gene markers without data. Please use the function drop.nullmarkers to remove markers that have no genotype data. After this, make a new summary to show the number of markers on each chromosome. How many gene markers were dropped? Where were the dropped markers located? Please use the data without nullmarkers for the following questions. One gene marker was dropped from chromosome 14.
hyper_new <- drop.nullmarkers(hyper)
hyper_new## This is an object of class "cross".
## It is too complex to print, so we provide just this summary.
## Backcross
##
## No. individuals: 250
##
## No. phenotypes: 2
## Percent phenotyped: 100 100
##
## No. chromosomes: 20
## Autosomes: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
## X chr: X
##
## Total markers: 173
## No. markers: 22 8 6 20 14 11 7 6 5 5 14 5 5 4 11 6 12 4 4 4
## Percent genotyped: 48
## Genotypes (%):
## Autosomes: BB:50.1 BA:49.9
## X chromosome: BY:53.0 AY:47.0nmar(hyper_new)## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 X
## 22 8 6 20 14 11 7 6 5 5 14 5 5 4 11 6 12 4 4 48. Please conduct single-QTL analysis and create a table to give the maximum LOD score on each chromosome based on their blood pressure phenotypes. Which gene marker has the highest LOD score? Which chromosome contains the gene marker that has the highest LOD score? Then, creates an interactive chart with LOD curves from a genome scan for all chromosomes. Chromosome 4 has the gene marker (D4Mit164) with the highest LOD score.
hyper_new <- calc.genoprob(hyper_new, step=1) #1centimorgan is being used
out.em <- scanone(hyper_new) #looks at the first phenotype by default
#out.em2 <- scanone(hyper, pheno.col=1:3) is used for data with more than 1 phenot and enables you to select columns of interestsummary(out.em)## chr pos lod
## c1.loc45 1 48.3 3.529
## c2.loc45 2 52.7 1.612
## c3.loc33 3 35.2 0.784
## D4Mit164 4 29.5 8.094
## c5.loc68 5 68.0 1.554
## c6.loc23 6 23.0 1.862
## D7Mit297 7 26.2 0.400
## D8Mit271 8 59.0 0.791
## D9Mit18 9 68.9 0.750
## c10.loc8 10 10.2 0.261
## c11.loc36 11 38.2 0.668
## D12Mit37 12 1.1 0.429
## D13Mit78 13 59.0 0.313
## D14Mit7 14 52.5 0.106
## c15.loc14 15 19.5 1.730
## D16Mit70 16 51.4 0.370
## D17Mit46 17 3.3 0.207
## D18Mit17 18 14.2 0.506
## D19Mit59 19 0.0 0.792
## cX.loc38 X 39.1 0.998summary(out.em, threshold=8)## chr pos lod
## D4Mit164 4 29.5 8.09iplotScanone(out.em)9. Based on your genome scan results, create a table which only includes those chromosomes with LOD > 1. Creates an interactive chart with LOD curves linked to estimated QTL effects for only those chromosomes with LOD > 1. Find the gene maker with the highest LOD score and describe how does the genetype of this marker influence the individual’s phenotype. The gene marker with the highest LOD score is D4Mit164. The chart demonstrates that the BB genotype is strongly associated with higher blood pressure, whereas BA is not.
summary(out.em, threshold=1)## chr pos lod
## c1.loc45 1 48.3 3.53
## c2.loc45 2 52.7 1.61
## D4Mit164 4 29.5 8.09
## c5.loc68 5 68.0 1.55
## c6.loc23 6 23.0 1.86
## c15.loc14 15 19.5 1.73iplotScanone(out.em, hyper_new, chr=c(1,2,4,5,6,15))10. Please save your interactive chart from Q9 as a html file hyper_iplotScanone.html and make sure your upload it to your github repository with your lab14 homework as well.
hyper_plot <- iplotScanone(out.em, hyper_new, chr=c(1,2,4,5,6,15))
htmlwidgets::saveWidget(hyper_plot, file="hyper_iplotScanone.html")Push your final code to GitHub!
Please be sure that you check the keep md file in the knit preferences.